Anum Ali

Estimating millimeter wave channels using out-of-band measurements

Channel estimation and beam training can be a source of significant overhead in establishing millimeter wave (mmWave) communication links, especially in high mobility applications like connected vehicles. In this paper, we highlight the opportunities and challenges associated with leveraging channel state information acquired at a lower frequency as a form of side information on a higher frequency channel. We focus on the relationship between spatial correlation matrices of sub-6 GHz and mmWave channels. We provide a transform that can be used to relate the spatial correlation matrix derived at one frequency to another much different frequency. We derive an expression for the excess mean squared error and use it to evaluate the performance experienced by using the transformed correlation in mmWave channel estimation.

Receiver-Based Recovery of Clipped OFDM Signals for PAPR Reduction: A Bayesian Approach

Clipping is one of the simplest peak-to-average power ratio reduction schemes for orthogonal frequency division multiplexing (OFDM). Deliberately clipping the transmission signal degrades system performance, and clipping mitigation is required at the receiver for information restoration. In this paper, we acknowledge the sparse nature of the clipping signal and propose a low-complexity Bayesian clipping estimation scheme. The proposed scheme utilizes a priori information about the sparsity rate and noise variance for enhanced recovery. At the same time, the proposed scheme is robust against inaccurate estimates of the clipping signal statistics. The undistorted phase property of the clipped signal, as well as the clipping likelihood, is utilized for enhanced reconstruction. Furthermore, motivated by the nature of modern OFDM-based communication systems, we extend our clipping reconstruction approach to multiple antenna receivers and multi-user OFDM.We also address the problem of channel estimation from pilots contaminated by the clipping distortion. Numerical findings are presented that depict favorable results for the proposed scheme compared to the established sparse reconstruction schemes.

Compressed Sensing Based Joint-Compensation of Power Amplifier's Distortions in OFDMA Cognitive Radio Systems

Linearization of user equipment power amplifiers driven by orthogonal frequency division multiplexing signals is addressed in this paper. Particular attention is paid to the power efficient operation of an orthogonal frequency division multiple access cognitive radio system and realization of such a system using compressed sensing. Specifically, precompensated overdriven amplifiers are employed at the mobile terminal. Over-driven amplifiers result in in-band distortions and out of band interference. Out of band interference mostly occupies the spectrum of inactive users, whereas the in-band distortions are mitigated using compressed sensing at the receiver. It is also shown that the performance of the proposed scheme can be further enhanced using multiple measurements of the distortion signal in single-input multi-output systems. Numerical results verify the ability of the proposed setup to improve error vector magnitude, bit error rate, outage capacity and mean squared error.

Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems

By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.

Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery

This paper presents a novel narrowband interference (NBI) mitigation scheme for single carrier-frequency division multiple access systems. The proposed NBI cancellation scheme exploits the frequency-domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data free to sense the NBI signal at the receiver. Furthermore, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between the NBI sources and the system under consideration. Toward this end, first, an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources, and second, the sparsity of the unknown signal is restored prior to reconstruction using a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data-points and using them as additional measurements. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.

Compressed sensing techniques for receiver based post-compensation of transmitter's nonlinear distortions in OFDM systems

In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifiers nonlinear distortions. HighlightsCompressed Sensing is used for receiver based power amplifier linearization.Accurate measurement based model of commercial power amplifier is used.Weighted compressed sensing is proposed for enhanced estimation.Bandwidth efficient iterative data aided algorithm is proposed.Data aided enhanced channel estimation scheme is proposed.Simulation of the channel effect on the performance of the developed algorithm.

Millimeter-Wave Communication with Out-of-Band Information

Configuring the antenna arrays is the main source of overhead in mmWave communication systems. In high mobility scenarios, the problem is exacerbated, as achieving the highest rates requires frequent link reconfiguration. One solution is to exploit spatial congruence between signals in different frequency bands and extract mmWave channel parameters with the aid of side information obtained in another band. In this article we propose the concept of out-of-band information aided mmWave communication. We analyze different strategies to leverage information derived from sensors or from other communication systems operating at sub-6 GHz bands to help configure the mmWave communication link. The overhead reductions that can be obtained when exploiting out-of-band information are characterized in a preliminary study. Finally, the challenges associated with using out-of-band signals as a source of side information at mmWave are analyzed in detail.

A Robust Least Mean Square Algorithm for Adaptive Array Signal Processing

Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.

Tracking Performance of Two Constant Modulus Equalizers

Exploiting the error variance relation, this letter discusses the evaluation of excess mean square error (EMSE) of constant modulus equalizers in a noise-free non-stationary environment. The EMSE analyses are presented for two equalizers - the CMA22 and the βCMA. Simulation results show a close match in theoretical and simulated errors for both algorithms.